Scene gist recognition is a critical early stage of scene perception. It is often operationalized in terms of categorizing scenes, such as label verification. Since Rosch, et al.'s (1976) seminal study, it has been assumed that basic level distinctions are made prior to superordinate distinctions, and a number of basic level scene categories have been identified (Tversky & Hemmenway, 1983). However, recent research has called into question the primacy of the basic level. Rogers and Patterson (2007) showed an object categorization advantage for the superordinate level over the basic level for speeded decisions, and Fei-Fei, et al. (2007) showed the “Indoor/Outdoor” scene distinction was perceived at shorter SOAs than basic level distinctions. Importantly, the Spatial Envelope model of scene gist recognition (Oliva & Torralba, 2001) assumes that the superordinate level “Natural/Man-made” distinction occurs prior to basic level distinctions, such as “Mountain,” “Forest,” or “Street.” The current study tested this assumption of the Spatial Envelope model.

Methods: We used a subset of Oliva and Torralba's scene images and their 2 superordinate (Natural, Man-made), and 8 basic level categories. Using a post-cued category verification task, level of categorization (Superordinate vs. Basic) was a between-subjects variable (N = 80). Images were briefly flashed (12 ms), and masked at 5 SOAs (12–72 ms) along with a no-mask control condition. SOA was a within-subjects variable.

Results & Discussion: Accuracy was greater in the Superordinate (Natural/Man-made) task than the basic level task (p = .002), and there was a strong main effect of SOA. Of primary interest, the superordinate (Natural/Man-made) advantage was greatest at the shortest SOAs, and decreased as processing time increased (p [[lt]].001), consistent with the Spatial Envelope model's assumption that the Natural/Man-made distinction is made earlier than more fine-grained basic level distinctions. Thus, more abstract categorical distinctions may be accessed earlier in scene gist processing.